Abstract
Evolutionary computation techniques are increasingly being applied to problems within Information Retrieval (IR). Genetic programming (GP) has previously been used with some success to evolve term-weighting schemes in IR. However, one fundamental problem with the solutions generated by this stochastic, non-deterministic process, is that they are often difficult to analyse. In this paper, we introduce two different distance measures between the phenotypes (ranked lists) of the solutions (term-weighting schemes) returned by a GP process. Using these distance measures, we develop trees which show how different solutions are clustered in the solution space. We show, using this framework, that our evolved solutions lie in a different part of the solution space than two of the best benchmark term-weighting schemes available.
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Cummins, R., O’Riordan, C. Evolved term-weighting schemes in Information Retrieval: an analysis of the solution space. Artif Intell Rev 26, 35–47 (2006). https://doi.org/10.1007/s10462-007-9034-5
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DOI: https://doi.org/10.1007/s10462-007-9034-5